Ditto AIBlog

How to spot for 'hype AI'

So say the experts at Gartner, and they should know – the research firm is the creator of the now-infamous ‘Hype Cycle’ reports, released each year to detail the levels of exaggeration and misplaced enthusiasm that genuinely innovative and emerging technologies are subject to. In some cases, the hype really isn’t backed by much. In others, it’s justified.

How, then, are investors supposed to tell the difference between genuine innovation and so-called ‘fake AI’: machine learning technology that is often just repackaged statistics software? We’ve got a few ideas to help cut through the noise.

AI has, perhaps unsurprisingly for a technology so bound up with sci-fi fantasy, become a buzzword in itself, one that skeptics like the Guardian’s John Naughton claim is ‘widely used (and abused), loosely defined and mostly misunderstood.’ Here’s our advice for tightening the definition and understanding when the hype has been earned.

Educate and cultivate

Reading up on the fundamentals of artificial intelligence is, unsurprisingly, the best way to equip yourself with the knowledge you need to spot ‘hype AI’. Get a handle on the fundamental definitions:

If you can answer questions like these, you’ll have a hype-clearing toolbox to work with.

It’s important to pay attention to your information sources when doing this, because they might not be as reliable as they seem. A 2018 study by Reuters and the University of Oxford found that a surprisingly large proportion (over a third) of articles from supposedly unbiased news outlets were based on sources within the AI industry. A healthy dose of skepticism and awareness of this fact wouldn’t go amiss.

Because of this, curating reputable sources goes hand-in-hand with educating yourself. How do you know when you’ve found them?

Avoid the anthropomorphic

‘Right now, AI doesn’t have free will and it certainly isn’t conscious – two assumptions people tend to make when faced with advanced or overhyped technologies...practically any computer program that automatically does something is [now] referred to as AI’

Watch for sources that make wild, starry-eyed claims about their technology, especially those that describe their ‘AI’ using words that make it sound like a human. If you spot a ‘he’ or ‘she’, or a ‘thinks for itself’, chances are it’s disingenuous marketing fluff.

If someone is claiming to have created ‘Artificial General Intelligence’ or that they’re selling ‘strong AI’, they’re probably not telling the full truth – at least not yet. Keep an eye out for those buzzwords and proceed with caution. It’s safer to bet on a company that’s open about their strengths, weaknesses and areas for improvement than a company who pretends they’ve got everything covered.

Look for real-world demos

It’s easy to make AI look good in a controlled setting, but it’s hard to make it work in real world situations – just look at autonomous cars, or Sophia the robot and the controversy surrounding how ‘intelligent’ she actually is.

Seek out developers that are transparent about their technology and its limitations, and that are willing to show you how it works. In the interest of said transparency, that’s exactly what we’re trying to do at Ditto with Explainable AI. We want it to be clear how and why our platform makes decisions, not to cloak it in hype and mystery.

It’s not all hype

Results aren’t magically arrived-at by superhuman AI. Machine learning is the result of expert input and painstaking rounds of iteration and improvement. This isn’t a reason to lose enthusiasm for AI, though. The reality is quite the opposite: As Amara’s Law dictates,

‘we often overestimate the impact of emerging technologies in the short run, and underestimate it in the long run.’

AI is going to make a huge difference to the way we live and work, and in many ways it’s already doing that – but it’s not going to do it as magically as some people would have you believe. Identifying the long-run, realistic success stories early means spotting and avoiding the hype.